{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=Crimson size=6 face=\"宋体\" align=\"center\">k均值聚类算法（k-means clustering algorithm）</font>     \n",
    "--------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "kmeans算法是无监督的聚类算法\n",
    "\n",
    "以二维模拟样本为例，使用Kmeans算法聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "import pylab\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入原始数据(import the dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "92a50b252f3341dfb8f08ae68093cd45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='选择数据集:', options=('testdata/dataset_producer.py', 'testdata/randse…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.show_base_data(filename: str)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据集存放位置\n",
    "DATA_DIR=\"testdata/\"\n",
    "all_data_path=[]\n",
    "for root, dirs, files in os.walk(DATA_DIR):\n",
    "    for file in files:\n",
    "        all_data_path.append(os.path.join(root,file))\n",
    "        \n",
    "#可交互文件选择模块\n",
    "iw_filename=widgets.Dropdown(\n",
    "    options=all_data_path,\n",
    "    description='选择数据集:',\n",
    "    disabled=False,\n",
    ")\n",
    "\n",
    "def show_base_data(filename:str):\n",
    "    with open(filename,mode='r') as file:\n",
    "        for line in file:\n",
    "            print(line)\n",
    "\n",
    "# ow_base_data=widgets.interactive_output(show_base_data,{'filename':iw_filename})\n",
    "interact(show_base_data,filename=iw_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 探索数据(explore the data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "对于给定数据集进行相关处理并展示\n",
    "\"\"\"\n",
    "def show_data_shape(x:np.array):\n",
    "    return\n",
    "\n",
    "def load_data(filename:str)->np.array:\n",
    "    \"\"\"\n",
    "    将txt文件中的数据转换成np数组\n",
    "    :param filename:模拟数据文件路径\n",
    "    :return:np数组\n",
    "    \"\"\"\n",
    "    dataset = []\n",
    "    file = open(filename)\n",
    "    for line in file.readlines():\n",
    "        lineArr = line.strip().split('\\t')\n",
    "        m = len(lineArr)\n",
    "        dataset.append(lineArr[0:m])\n",
    "    data=np.array(dataset,dtype=np.float64)\n",
    "    print (\"该数据集有%d个%d维数据\"%(data.shape[0],data.shape[1]))\n",
    "    return data\n",
    "\n",
    "def draw_dataset(x:np.array):\n",
    "    \"\"\"\n",
    "    用matplotlib绘制数据\n",
    "    :param x:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    #可视化一下\n",
    "    plt.scatter(x[:,0],x[:,1],marker='x',color = 'r')\n",
    "    plt.xlabel('x0')\n",
    "    plt.ylabel('x1')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18f3b8a855d64773b77d0692bb641059",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='选择数据集:', index=1, options=('testdata/dataset_producer.py', 'testda…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.choose(filename: str)>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def choose(filename:str):\n",
    "    \"\"\"\n",
    "    数据集选择流程\n",
    "    用户通过下拉菜单选择数据集\n",
    "    进行相关处理并展示\n",
    "    :param filename:widget传入的文件名\n",
    "    \"\"\"\n",
    "    pylab.rcParams['figure.figsize'] = (6,4)\n",
    "    x = load_data(filename)\n",
    "    draw_dataset(x)\n",
    "\n",
    "\n",
    "# ow_data_shape=widgets.Output().append_stdout(\"该数据集有%d个%d维数据\"%(np_data.shape[0],np_data.shape[1]))\n",
    "# ow_draw_data=widgets.interactive_output(choose,{'filename':iw_filename})\n",
    "# widgets.HBox([widgets.VBox([filename_widget,ow_data_shape,ow_draw_data]),ow_base_data])\n",
    "interact(choose,filename=iw_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Kmeans算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始化聚类中心\n",
    "\n",
    "TODO:\n",
    "1.更新K值全部初始化中心点\n",
    "2.按钮添加一个随机中心点\n",
    "3.输入坐标添加中心点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def randCentroids(x:np.array,k:int)->np.array:\n",
    "    \"\"\"\n",
    "    随机初始化centroids ,随机选择k个样本点作为质心\n",
    "    :param x:numpy_data\n",
    "    :param k:随机选取k个\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    m,n = x.shape # m=80个数据，n=2维\n",
    "    centroids = np.zeros((k,n)) #产生k个 [0 0]的数据\n",
    "    randIndex = np.random.choice(m,k) #从m个数据中选k个randindex\n",
    "    centroids = x[randIndex]\n",
    "    return centroids\n",
    "# centroid_widget=widgets.\n",
    "#可交互K值选择\n",
    "K_widget=widgets.IntSlider(value=4,min=1, max=6, step=1, continuous_update=False)\n",
    "interact(randCentroids,k=K_widget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算距离\n",
    "def computeDistance(A,B):\n",
    "    return np.sqrt(np.sum(np.square(A-B)))\n",
    "\n",
    "#计算x的均值，改变centroids\n",
    "def change_centroids(x,idx,K):\n",
    "    m,n = x.shape\n",
    "    centroids = np.zeros((K,n))\n",
    "    for i in range(K):\n",
    "        index = np.where(idx[:,0].ravel() == i)\n",
    "        centroids[i] = np.mean(x[index],axis=0)\n",
    "    return centroids\n",
    "\n",
    "def plot_data(x,idx,K,cent,centroids,cnt):\n",
    "    m=x.shape[0]\n",
    "    tmp_idx=[]\n",
    "    color_list=['r','g','y','b','m','orange']\n",
    "    tmp_idx=idx[:,0].tolist() #要把numpyfloat64转换为list int\n",
    "    for i in range(m):\n",
    "        plt.scatter(x[i,0],x[i,1],marker='x',color=color_list[int(tmp_idx[i])])\n",
    "    \n",
    "   # plt.scatter(centroids[:,0],centroids[:,1],marker='o',color = 'black',linewidths=10)\n",
    "    \n",
    "    for k in range(K):\n",
    "        plt.scatter(centroids[k,0],centroids[k,1],marker='o',color = color_list[k],linewidths=10)\n",
    "        plt.plot(np.array(cent[\"%d\"%(k)])[:,0],np.array(cent[\"%d\"%(k)])[:,1],\"b-o\",color=color_list[k])\n",
    "    plt.xlabel('X1')\n",
    "    plt.ylabel('X2')\n",
    "    plt.title(\"figure %d\"%(cnt))\n",
    "    plt.savefig(\"figure %d.jpg\"%(cnt),dpi=300)\n",
    "    plt.show()\n",
    "        \n",
    "\n",
    "#为数据集x找到最近的质心,直到质心不变为止   \n",
    "def kmeans(x,K):\n",
    "    #1.初始化质心\n",
    "    centroids = randCentroids(x,K)\n",
    "    m = x.shape[0]\n",
    "    idx = np.zeros((x.shape[0],2)) #记录x对应的质心的下标 + 平方误差\n",
    "    clusterchanged = True #记录分类变没变化\n",
    "    cent = {} #用字典，来存储各个质心的变化\n",
    "    cnt=0 #记录分配点的次数\n",
    "    \n",
    "    # 初始化质心字典\n",
    "    for k in range(K):\n",
    "        cent[\"%d\"%(k)] = []\n",
    "        cent[\"%d\"%(k)].append(centroids[k,:].tolist())\n",
    "        \n",
    "    while clusterchanged:\n",
    "        \n",
    "        clusterchanged = False\n",
    "        #2.为每个样本点分配质心\n",
    "        for i in range(m):\n",
    "            minDist = np.inf\n",
    "            minIndex = -1\n",
    "            for j in range(K):\n",
    "                distance = computeDistance(x[i,:],centroids[j,:])\n",
    "                if distance<minDist:\n",
    "                    minDist = distance\n",
    "                    minIndex = j\n",
    "            if idx[i,0] != minIndex:\n",
    "                clusterchanged = True\n",
    "            idx[i,:] = minIndex, minDist**2\n",
    "        #分配完点之后的情况输出一下\n",
    "        cnt=cnt+1\n",
    "        plot_data(x,idx,K,cent,centroids,cnt)\n",
    "        #3.更新质心的位置\n",
    "        centroids = change_centroids(x,idx,K)\n",
    "        # 4.更新质心字典\n",
    "        for k in range(K):\n",
    "            cent[\"%d\"%(k)].append(centroids[k,:].tolist())\n",
    "    plot_data(x,idx,K,cent,centroids,cnt)\n",
    "    \n",
    "    return centroids,idx,cent,cnt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SMAVEzXG3Q+/Zc25evpVUTaXVSOvXl84JABwNUNWJZgaB5nEEQP4FWUGzfDWSWyl03bqZYnVhVQO1+bQyKsGVO8FiACB7uOWrAWdFkFtBdPPm8KqB2nxaGZ0jjrttbcYUENnF/QP2rghyA0HQK4K49DSW0r5yJ0hcBUT2imJFkHfuwcWlp5QwXAVE8RLViiCWnaYUMxYARORSEXlcRJ4TkWdEJGuqLWSZKKuBcumpFeJ0kHqSmBwB5AF8WVU/AGA5gDtE5CqD7SFb1Du+Msg5AJadNs7mc4yTztgksKq+AuCV4n+/KSLPAegE8KypNpFFatUnCgrLThvnPUMAQEn9ID+lHag5VkwCi8gSAH8P4GpVPV32vdsB3A4Al112We/x48cjb19iNFqSOW34czGKpR3CZ+0ksIjMAfAjAOvLO38AUNX7VLVPVfsWLFgQfQOTguvdqwtyQxs1jaUdzDEaAETkPDid//dV9a9NtiXRvOvdyw9+D3pjFVGTTJ9jnGbG5gDECe9/DuA5Vd1qqh2pwFLLZKlGzxCgcJgcAXwEwG0AbhSRkeLXSoPtSTaudycL1S7tcOE5ZaUpWCZXAf0jAPY+rWrmrN1K690ZBMiwSqUd2touRD7/xvT97kihre1C/Pqv3zP9XK4Sao3xSWBqQaMTu1zvTpYrv9KfmnqjpMKnmxb65S93o1AoTD+O+wVawwAQV81M7Ea1sYooAN400Pj4IPbvz2B8fBCzZ/fg7NkRHDmysSQo5POTTA/5ZMU+gEaxGFyZZguZcb07xYiqYv/+mWvU666bwpEjG7lfwAdr9wFQC5qd2OV6d4qJSktDjxzZiMsvL10wyM6/NQwAccZCZpRAtU79Gh7uLXks9wu0hgEgrjixSwlVaWno5ZdvnZ4D4FGQweGJYHHFQmaUYOVLQzOZDObPX4WOjv6S/QIAeBRkCzgJHHec2KUUKV/3z30AjeEkcFJxYpdSpLyzt63zj9vBNgwAREQBiOPBNgwAREQt8h5sU7572eaNapwEJiJqkXdSenx8cHqzmu0b1TgCICIKQBwPtmEAICIKQBwPtmEAICJqUa3dyzYHAc4BUGO434CoqmoH2wB2b1RjAKD6cjmnxLS749gtQ9HRwUPliYoqHWzDOQCKNx4oT9Qw2zeqleMIgGrjgfJEicVaQNQYVSDjGTAWCuz8iWKCtYDIP547QJRIDABUG88dIEqsmnMAIjIXwAJVPVJ2/wdV9WehtozswHMHiBKr6hyAiHwawHYAvwBwHoA1qvrT4veeVNWlkbWyiHMABnEfAFFs+ZkD+CMAvaraA+D3APyFiHzKfb0Q2kg247kDRIlTKwXUrqqvAICqPiEiNwD4iYgsAsDELxFRzNUaAZwWkcvdG8VgcD2AWwH8ZsjtIiKikNUKAH+AslSPqr4JYADAn4TZKCIiCl+tAHA/gH8vItNpIhG5GMBOAP827IYREVG4agWAXgBdAJ4SkRtFJAvgCQBDAJZF0TgiIgpP1UlgVX0dwH8sdvyPAXgZwHJVPRFV44iIKDxVRwAi0iEi/x3OEtABAP8bwEMicmNUjSMiovDUSgE9CeBFAH2q+oiqrgdwG4A/FpEfBvHmIjIgIs+LyKiI/GEQr0lERI2ptQ/guvJ0j6qOAPgtEfliq28sIm0AvgPg4wBOAPipiOxW1WdbfW0iIqqv6gigVq5fVb8XwHt/GMCoqh5V1XcA/BWcPQZERBQBk9VAOwG85Ll9onhfCRG5XUQOicihiYmJyBpHRJR0JgNApWIy55SYUNX7VLVPVfsWLFgQQbOIiNLBZAA4AeBSz+1FcJaaEhFRBEwGgJ8C+Bci0iUi5wP4DIDdBttDRJQqxg6FV9W8iNwJ4GEAbQD+p6o+Y6o9RERpYywAAICq7gGwx2QbiOJGVSGe8xjKbxM1imcCE8XI2FgOo6Mb4J7kp6oYHd2AsbGc2YZRLDEAEMWEqiKfn8T4+OB0EBgd3YDx8UHk85OodrwrUTVGU0BE1DgRQXf3NgDA+PggxscHAQCdnVl0d29jGoiaxhEAUYx4g4CLnT/5xQBAFCNu2sfLOydA1AwGAKKY8Ob8Ozuz6O8voLMzWzInQNQMzgEQxYSIoL29oyTn76aD2ts7mAaylM3LdhkAiGKkqytX0oG4QcCWDoVKjY3lkM9PTv8/ckdx7e0d6OrKmW4eU0BEcVPe2bPzt1Mclu1yBEBEFII4LNvlCICIKCS2L9tlACAiConty3YZAIiIQhCHZbucAyAiCkGry3ajWD7KAEBEFBK/y3ajWj7KFBARUYiaXbYb5fJRjgCIiCwS5fJRjgCIiCwT1fJRBgAiIstEtXyUAYCIyCJRLh/lHAARkUWirPrKAEBEZJmoqr4yBUREZKEoqr4yABARpRQDABFRSjEAJET5ygAbCk0Rkd0YABIgty+HDQ/PLA9TVWx4eANy+3JmG0ZEJWy7UGMAiDlVxeTbkxg8ODgdBDY8vAGDBwcx+bYdx84RkVPgzbuO313vPzaWM9YmLgONORHBtpudNcKDBwcxeNCpG5JdlsW2m+05eYgozbwF3gCnrIN3s1cYpZ4bIXG6Quzr69NDhw6ZboaVVBWZLTMDusKmAjt/skIUde3jwLvD1xXV+cAiMqyqfeX3MwWUAG7ax8s7J0Bkio1pD1NsPB/YSAAQkW+KyD+LyM9E5G9EpMNEO5LAm/PPLsuisKmA7LJsyZxAGO9Z6zYREG1d+ziw8XxgU3MAjwL4qqrmReRPAXwVwB8YakusiQg6ZnWU5PzdOYGOWcHWDQGcFUeTb09Ov5cbgDpmdSB3fS7Q96J481vXPokpo/ICb945AMDcSMBIAFDVRzw3DwD4bRPtSIrc9efWDQljAti74ggAtt28rWT0kYQ/VAqWGwS8ee9anV1URyFGLcoCb82wYRXQWgD/q9o3ReR2ALcDwGWXXRZVm5pm+qoljLohlT4DVxxRM6qlPSoFAVtXygQlqgJvzQhtFZCIPAZgYYVv3a2qf1t8zN0A+gB8ShtoiK2rgJKYFqn1mTb3b+aKI6qrVtqjWhrI5EqZJIt8FZCqfkxVr67w5Xb+nwdwC4DfbaTzt5WJjVh+J2EbfV6tz/T6269j/d71JY/niiOqpFrao7MzWzXtYeNKmSQzsg9ARAYAbAXQr6oTjT7P1hGAt4N0hZUW8TvaaPZ5lT7TumXrAAV2PLFj+vN55wCYBqJKmkmPcgQQDtv2AXwbwHsBPCoiIyJyr6F2BMK78sYV9iRsM6MNP8+r9Jm237wdF11w0TkrjrLLsqGsOKJkaHR+KsqjEMlhahVQt4n3DUu1jVhBBgH3qsnPJKyf59X6TO5rel+bnT+1ytaVMommqrH56u3tVdsUCgXNPpRV5KDZh7IVb7dq8+ObS15rampKkcP0V6PvUSgUGnpeFJ+JqJry3y/+vrUOwCGt0KeyFESLqm3ECiotomXpm0KhgN7v9ZY8ppFJWG2iXETYn4moliiOQqSiSlHB1i8bRwCuMK9avFfg7lfPvT06NTXV0JW53yt6XolR0Pg7VVnYPxdUGQHYsBEsEcK8anGvwL0rcoa/OIxMJtNQ2Qe/5SJ4JUZBSuou31aZ/LkwAMSAVkjfbHxkY0lnXq9zjqpcBFElqsHs8i1/XKPPs1VQPxe/GAAs53b+3rX23vX5zXTivKInU/wWhvNK4ggiiJ9LKzgJbDk/E7KqLNdM9mlll6/3SjlppaVN7n7miWAx0ejQN4l1iSgZvJ22q5kr3Vafb6soPpdtO4GpSY2kb1R5QDzZydvJ+d3lm8Q6QUH8XFrBOYAGxGXiye9OYaKwBbHL1+0svaqVlo4L07ufmQKqI8qUSlCBRpUHxJOd/P6Ol18pN1JaOk7CvshkCsiHKFMquX25kp257nvl9uWabjMPiCcbVFqM4Hclmp/S0nFiaoUeU0A1RJVS8QYawP9Ri0EuGSVqRRhLNm08USvuGADqqLQLN+iONKhA43fHbzXlgadQKCCTyVT9PhEQ7uYm7mUJFucA6vBeVbvCmlQNKncfRD6xfO5j8+ObsfuF3Vh1xSrcc8M9XF5KAKr/riV1yWZccQ7Ah/KUSmFTAdll2ZI5gSDfa/3DpUctrn94va/3aPUqqXzuo1AoYPcLuzFycgS7X9iNQqHA5aVUc97K1iWb3CRZiimgGoJOqVSjqljx5ytwcPwg1i1bh+03b8f6h9djx8EdOHjiIIa+MDT9XlGkXaqlpHoW9mDk5Ajavt4GgMtL06zevFWhUMCRIxtLntPMks0wVsUksZREqxgA6oi8iJqW/nts8ljJsDqqtEu1CqRu5w9wUjlMtu89qTVvtfWmrThyZGPFJZtA/ZFAGB216aJrtkpVCsjv8C/siScRwdAXhrDuw+uw44kdyGzJYMcTO3Dtxdfi1bOvYuMjGyPf1VtpOamfg2ioeUEtCQ6bNwi4tt28DZlMxveSzWZq/jTz9+xtw/j4IPbvzyRmD0FLKh0SYOtXKwfClB+r6B6Ksvnxzb5fM2jlRzZ6D3xxv6I4krH8wJipqSntuben6YNoqHlxOo6z0kFF5X9j5Y9v9HVfeCGrjz+O6a8XXij97EePbi65z33O0aOb676293Vt+nmGCWk+ElIj3NDVShsr1fzfetPWkvuiSLuUz31kMhmsumIVehb2YNUVq6YPouERkcHzVnsdPDiIzJZMyb4OW37W3r+hagskWtn0VWsCWZsYJZS3uVIpCRv+/k1JzTJQ7y+sy5Y/qvI/Ju+Emjvx6oqyzeV/xO4+APf+8n8pOBrQkuAwhVUmxduhu8pTNY08ptprJrGURD3VloEaT+s089XqmcDlKRabhn/lKSpv2sWmVEAcUmlxVy+1YpOgz7L1pn/cFE/5be9jm0nn+E0bJQHSfiawVkixbHh4gxUjAODc1UZu2qV/cX+gS1C971Hpdr3nBlGygqpzf0/jUs4j6AUSjVbH1CrpnFpX8iwlUUGlqGDrl98RQJwm1soFeYUVxNV7nK5O44qjrNq/95VGBc8/v67kNn8fSyHNI4CoNnSFIagrLA3o6j2K2khpF/neEwvV+r0vHyUcO3YPRID3v38d2ts7ACD1G7walZpJYMDc5pqpwhRGTo7gwIkDOPXWKcy7YB6WL1qOnoU9aMu01X+BgGgAE+FBvAZRENy+a2Yydx26u7enanK3UdUmgVMVAKKmqtg1sgtb/n4Ljk0eO+f7SzqWYNN1m7CmZ01kv6TawuoSb+dfnp9mECBTtMaKIIAVQwEWg4vcmXfOYOUPVmLt7rUVO3/AKfOwdvdarPzBSpx550zobXI7cK9mdvNWS6VxPwCZVG3fAOCMDsbGcgZaFQ+pmAOImqpi9YOrsXd0b0OP3zu6F6sfXI09n90TWida6+odaDyPz/w02cYZAZRW0h0dXQ9V4OWXd6S61k89DAAh2DWyq+HO37V3dC/uP3w/1vSsCaVNQU6E81AOssVM+mcHOjvXTXf64+M7ADgTw7U2h9lccC8KnAMI2FRhCt3f6q6a9qmlq6MLL37pxVAnhvlL3zr+DO3irR4KAPv3z2S2+/sLFXerp600tJVzACLyFRFREZlvsh1BGjk54qvzB4CxyTEcfvVwsA0qw6v31theqbP8gi5OF3h+dXXlSnL+XqOjzoFG3rkAVX+1hJLIWAAQkUsBfBzAz021IQwHThxo6flDLw0F1BIKmncvhY1FBW0PTmFzO/HZs3sAALNn92B8fBDDw70lnTtLQ88wOQLYBuAuzByBkgin3jpl9PkUHpsrddoenMLm3RzW2zuMzs4szp51iiiePTtyTuder+JoWhgJACKyCsC4qtbNd4jI7SJySEQOTUxMRNC61sy7YJ7R51O4qh2CYrrjsDk4RcVNBWUymbqdu5v28UpjaejQAoCIPCYi/1Th61YAdwPY1MjrqOp9qtqnqn0LFiwIq7mBWb5oeUvPX3HpioBaYpek5KZb3UsRJluDU5S8E7pe3s7dm/Pv7Myiv78wnQ5KWxAILQCo6sdU9eryLwBHAXQBOCwixwAsAvCkiCwMqy1R6lnYgyUdS3w9t6ujC9defG2wDbJAUnLT5XspKh2CYkP7vGxoV5Qa6dyrVRxt5MjKpIl8H4CqPg3gfXhRZQ0AAATkSURBVO7tYhDoU9XXom5LGNoybdh03Sas3b226edu6t8UaW2gKARVhM4GNhcVDGqjX9w1Wk6apaEdxvcBNBMA4rAPAHD+GFf+YGVTm8EGugdC3QlsUtIKyNm6DyCsE7riyNb/R6awGFzEzrxzpuFyEAPdA3hw9YOYc/6cCFpmRitF6Khx7PioEis3giXZnPPnYM9n92DnrTvR1dFV8TFdHV3YeetO7PnsnsR3/mnPTUeFG/2oGawFFCIRwZqeNbjtg7fh8KuHMfTS0PR5ACsuXYFrL742cTn/csxNE9mLASACbZk2LL1kKZZestR0UyJn88QpUdpxDoAiwdw0kTmcAyCjmJsmsg8DABFRSjEAEBGlFAMAEVFKMQAQEaUUAwARUUoxABARpRQDABFRSsVqI5iITAA4brodEZgPIBHlsRvAz5pMafmscfmci1X1nBO1YhUA0kJEDlXatZdE/KzJlJbPGvfPyRQQEVFKMQAQEaUUA4Cd7jPdgAjxsyZTWj5rrD8n5wCIiFKKIwAiopRiACAiSikGAMuJyFdEREVkvum2hEVEviki/ywiPxORvxGRDtNtCpKIDIjI8yIyKiJ/aLo9YRGRS0XkcRF5TkSeEZGs6TaFTUTaROQpEfmJ6bb4wQBgMRG5FMDHAfzcdFtC9iiAq1X1gwBeAPBVw+0JjIi0AfgOgE8AuArA74jIVWZbFZo8gC+r6gcALAdwR4I/qysL4DnTjfCLAcBu2wDcBSDRM/Wq+oiq5os3DwBYZLI9AfswgFFVPaqq7wD4KwC3Gm5TKFT1FVV9svjfb8LpGDvNtio8IrIIwL8B8D9Mt8UvBgBLicgqAOOqeth0WyK2FsBDphsRoE4AL3lun0CCO0WXiCwB8CEAB822JFTb4VygFUw3xK920w1IMxF5DMDCCt+6G8AfAbgp2haFp9ZnVdW/LT7mbjhphO9H2baQVTr8ONEjOhGZA+BHANar6mnT7QmDiNwC4BeqOiwi15tuj18MAAap6scq3S8i1wDoAnC4eHj6IgBPisiHVfVkhE0MTLXP6hKRzwO4BcBHNVmbU04AuNRzexGAlw21JXQich6czv/7qvrXptsToo8AWCUiKwHMAjBXRP5SVT9nuF1N4UawGBCRYwD6VDUOVQebJiIDALYC6FfVCdPtCZKItMOZ2P4ogHEAPwXwWVV9xmjDQiDO1cr9AE6p6nrT7YlKcQTwFVW9xXRbmsU5ALLBtwG8F8CjIjIiIveablBQipPbdwJ4GM6k6ANJ7PyLPgLgNgA3Fv8/jhSvkMlSHAEQEaUURwBERCnFAEBElFIMAEREKcUAQESUUgwAREQpxQBA1IRixcsxEZlXvH1R8fZiEdkrIpNxrQxJ6cMAQNQEVX0JwHcBfKN41zcA3KeqxwF8E846eKJYYAAgat42AMtFZD2AfwXgvwGAqv4fAG+abBhRM1gLiKhJqvquiPxnAHsB3FQs80wUOxwBEPnzCQCvALjadEOI/GIAIGqSiPTAOaltOYANInKJ4SYR+cIAQNSEYsXL78Kpdf9zOBO//9Vsq4j8YQAgas4XAfxcVR8t3v4zAL8hIv0i8g8AHgTwURE5ISI3G2slUQNYDZSIKKU4AiAiSikGACKilGIAICJKKQYAIqKUYgAgIkopBgAiopRiACAiSqn/D6gCc1RIj8nwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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02bL0gnRuzwjKnHpqXYlUItBQ2ayB5vHjm3Hq1C7EYiMjN3PHTQwA5C/WH7B9RtCGDal/uz0jiFNPA8ma6nnq1C6IjIjczB03MQCQP1nTQu3loN2eDsqpp4GVWglcBZHUMCY7f2c4CEz+5NWMIJadDiTVdxGLlVcGggwGABGZJiLbRWSviLwsInFTbSGfyTYjqJjtK8v5LDtOPfVc5gyeQjN6ksl3BstAkHMmrwD6APy1qs4GMB/Al0TkMoPtIb8otH2lm2MAXgUaysnJpiyq75ZdCI4MjgGo6hsA3hj492kR2QugDsArptpEPlJqfSInWHbaOKu0g30aZzGlHVIpIF4BlMsXg8AiMhPA1QCey/LYEgBLAGD69Ometit0gjbfvdT6RE54EWgop0J7COQa3E2lgHgFUC7jg8AiUgPghwCWq+qpzMdV9RFVnauqcydPnux9A8MikUhPa1jpD8519ybQUE5OtolkCsgdRgOApP4P/hDA91T1RybbEmr2+e7ZNn5nrpsMyrWHQL6BYNV3mAJygbEUkKTC+3cA7FXVtabaEQmc704+5XRTlmSSVwBuMHkF8CEACwHcKCKdAz+3GGxPuHG+O/lQrj0E6uriqK6eMKys9NC/OQjsBpOzgH4FgL1PuYod2M238TuDABmUbVOWqqoJ6Os7OXi/daVQVTUBf/In90M1NQjs9l4DUWN8EJjKUOzALue7k89lnun3959ET0/b4FiAlRb64x+fRDKZHEwBFVovQPn5YhooOVBKITPOd6cAyTU1dOzYRpw924n9+1dC9R2cP38EJ08+62grSErhlpBBZj+zt+Qb2A3aOgCKNFXFzp1DSYrrr+/H/v0rBwMCwK0gi8UtIcOo1IFdznengMg2NXT//pWYNSt9wiA7//IwAAQZC5lRCGVODW1pSaKuLo6enjZ0dDSlPZdbQZaHASCoOLBLIZVtauisWWsHxwDq6uIYM6YBY8ZckjZQTKXjIHBQcWCXQixzamgsFsOkSbehtrYFDQ3rsHv3j1FbewMmTryZW0GWgYPAQceBXYoQKyj8139djPe+909xySWPsPMvAgeBw4oDuxQhVmefKgY30nedf6kb25jGAEBEgePHctBONrYxjQGAiALHb3sC2ze2yVy93NfX69srAQ4CE1HgWCkgv3C6sY1pvAIgokBRVaj2+S4F5GRjG9MYAIgoUFTfBQBfpYAAZxvbmMYAQESBkky+AwC+SgHlW73s5yDAMQAqDtcbkE9YVwB+SgHl2tgGgK8XqjEAUGGJRKr0tLXi2CpDUVvLTeXJc0MpIP9cAQDZN7bhGAAFGzeUJ59Q7cfp0x14/fVHAAAnT/4Kp093QLXfcMuGZHb2fu78AZaCoGKUuu8AkYtUFUePbsKhQ2vw9tsHhz0+evRMzJixClOmLPJ9h2tKrlIQDABUHFUgZrtgTCbZ+VPF9fWdwSuv3Injx7cWfO7Eia247LInUF1d40HLgoW1gMg57jtABqhq0Z0/ABw/vhWvvHKnb2fc+BEDAOXHfQfIkKNHNxXd+VuOH9+Ko0cfq1CLwifvLCARGQ9gsqruz7j/SlX9dUVbRv7AfQfIANV+HDq0xtFrDx1agylTFkKkyuVWhU/OACAidwFYD+APkppwu0hVXxh4eBOAOZVvHvlCIpE+798KAuz8qULOnOnMOuBbjLff7saZM3swbhy7qELypYD+DkCTqjYC+CsA/yYidww8xr/8qOG+A+ShU6d2l/n6XS61JNzypYCqVfUNAFDV50XkBgA/E5GpAJj4JaKKeffd40ZfHxX5rgBOicgs68ZAMPgwgNsBfKDC7SKiCBsxYqLR10dFvgDwN8hI9ajqaQCtAL5WyUYRUbSNHz+/zNc3u9SScMsXAB4D8N9FZDBNJCIXAngUwMcr3TAiiq6amkaMHj3T0WtHj65HTc1V7jYopPIFgCYA9QBeEpEbRSQO4HkAuwDM86JxRBRNIlWYMWOVo9fOmLGKU0CLlHMQWFVPAPjCQMf/DIDXAcxX1SNeNY6IomvKlEU4duzxkhaDTZzYiilTPl3BVoVLzisAEakVkW8iNQW0FcD/BbBFRG70qnFEFF0igssuewITJ7YW9XyrFhALwhUvXwroRQC/BzBXVZ9S1eUAFgL4BxH53258uIi0isirItIlIn/rxnsSUXhUV9fgiis249JLH8Xo0fVZnzN6dD0uvfRRXHHFZhaCK1G+dQDXZ6Z7VLUTwLUi8rlyP1hSSbqNAG4CcATACyLypKq+Uu57E1F4iAguumgRpkxZiDNn9uDUqV14993jGDFiIsaPb0ZNzVXM+TuUbwwgZ65fVb/lwmd/EECXqh4AABH5AVJrDBgAiGgYkSqMGzeHJR5cZLIaaB2Aw7bbRwbuSyMiS0SkXUTajx075lnjiIjCzmQAyDZSM6zEhKo+oqpzVXXu5MmTPWgWEVE0mAwARwBMs92eitRUUyIi8oDJAPACgP8mIvUiMhLAJwE8abA9RESRkndDmEpS1T4RWQpgG4AqAN9V1ZdNtYeIKGqMBQAAUNXNADabbANR0Khq2mKnzNtExeKewEQB0t2dQFfXisGNz1UVXV0r0N2dMNswCiQGAKKAUFX09fWip6dtMAh0da1AT08b+vp6B4MCUbGMpoCIqHgigoaGdQCAnp429PS0AQDq6uJoaFjHNBCVjFcARAFiDwIWdv7kFAMAUYBYaR87+5gAUSkYAIgCwp7zr6uLo6Ulibq6eNqYAFEpOAZAFBAigurq2rScv5UOqq6uZRrIp/w8bZcBgChA6usTaR2IFQT80qFQuu7uBPr6egf/H1lXcdXVtaivT5huHlNAREGT2dmz8/enIEzb5RUAEVEFBGHaLq8AiIgqxO/TdhkAiIgqxO/TdhkAiIgqIAjTdjkGQERUAeVO2/Vi+igDABFRhTidtuvV9FGmgIiIKqjUabteTh/lFQARkY94OX2UVwBERD7j1fRRBgAiIp/xavooAwARkY94OX2UYwBERD7iZdVXBgAiIp/xquorU0BERD7kRdVXBgAioohiACAiiigGgJDInBngh0JTRORvDAAhkNiRwIptQ9PDVBUrtq1AYkfCbMOIKI3fTtQYAAJOVdH7di/anmsbDAIrtq1A23Nt6H3bH9vOEVGqwJt9Hr8137+7O2GsTZwGGnAignULUnOE255rQ9tzqboh8XlxrFvgn52HiKLMXuANSJV1sC/2qkSp52JIkM4Q586dq+3t7aab4UuqitiaoQu65KokO3/yBS/q2geBfYWvxav9gUWkQ1XnZt7PFFAIWGkfO/uYAJEpfkx7mOLH/YGNBAAR+YaI/E5Efi0iPxaRWhPtCAN7zj8+L47kqiTi8+JpYwKV+Mx8t4kAb+vaB4Ef9wc2NQbwNICvqGqfiPwzgK8A+BtDbQk0EUHt6Nq0nL81JlA72t26IUBqxlHv272Dn2UFoNrRtUh8OOHqZ1GwOa1rH8aUUWaBN/sYAGDuSsBIAFDVp2w3dwP4cxPtCIvEh4fXDanEALB9xhEArFuwLu3qIwx/qOQuKwjY8975OjuvtkL0mpcF3krhh1lAiwH8n1wPisgSAEsAYPr06V61qWSmz1oqUTck2zFwxhGVIlfaI1sQ8OtMGbd4VeCtFBWbBSQizwCYkuWh+1T1JwPPuQ/AXAB3aBEN8essoDCmRfId0+qW1ZxxRAXlS3vkSgOZnCkTZp7PAlLVj6rq5Vl+rM7/0wBuBXB3MZ2/X5lYiOV0ELbY1+U7phNvn8DyrcvTns8ZR5RNrrRHXV08Z9rDjzNlwszIOgARaQWwFkCLqh4r9nV+vQKwd5CWSqVFnF5tlPq6bMe0bN4yQIENz28YPD77GADTQJRNKelRXgFUht/WATwAYByAp0WkU0QeNtQOV9hn3lgqPQhbytWGk9dlO6b1C9bjgjEXDJtxFJ8Xr8iMIwqHYsenvNwKkVJMzQJqMPG5lZJrIZabQcA6a3IyCOvkdfmOyXpP+3uz86dy+XWmTKipamB+mpqa1G+SyaTGt8QVCWh8Szzr7XKt3r467b36+/sVCQz+FPsZyWSyqNd5cUxEuWR+v/h9Kx+Ads3Sp7IURJlyLcRyKy2iGembZDKJpm81pT2nmEFYLaFcRKWPiSgfL7ZCpAHZooJff/x4BWCp5FmL/Qzc+ml8uFH7+/uLOjN3ekbPMzFyG79T2VX694IcVwB+WAgWCpU8a7HOwO0zcjo+14FYLFZU2Qen5SJ4JkZuCusq33KZ/L0wAASAZknfrHxqZVpnXqhz9qpcBFE2qu6s8s18XrGv8yu3fi9OMQD4nNX52+fa2+fnl9KJ84yeTHFaGM4ujFcQbvxeysFBYJ9zMiCrynLN5D/lrPK1nymHrbS0ydXP3BEsIIq99HWjLlF/sh+dRzux+8huHD93HBPHTMT8qfPROKURVbEqtw6JIsbeaVtKOdMt9/V+5cVx5VoJzBRQQBSTvlEtr1yzqmJT5yaseXYNDvYeHPb4zNqZWHX9KixqXBToPzjynr2Tc1oPv9TS0kHgxu+lHAwARQjKwJPTlcIAcOadM7jziTuxtWtrzucc7D2IxU8uxuOvPI4n7nwCNSNr3D0ACi03VvlanaVdrtLSQWF69TNTQAV4WerZrUCjWtoG8aqKW75/S97OP1NrQys2f2pzYP/wyAyn3/F8Z8phSQNV8iTTb8XgAsGeUql0qefEjkTaylzrsxI7EiW3udQN4jd1biqp8weArV1b8diex0p6DUVLtskITmeiOSktHSSmZugxBZRHOSmVUpSbu7e/T6lTRvuT/Vjz7BpH7V6zcw0WXrmQA8M0TCWmbPpxR62gYwAoINsqXLcXULkVaJys+O082pl1wLcY3b3d2PPmHsy5aI6j11M4VXJxE9eyuItjAAXYz6otldr8pNTcfb73KTafuPH5jVi6ZWnpjR1wc8PN2Hz3Zsevp2DL9V0L65TNoOIYgAOZKZXkqiTi8+JpYwJuftbybelbLS7fttzRZ5RylnT83PGS39/uxNsnAr0Ih5zr7k6kbdRidfrd3Qmji5vy4SLJdAwAeXhVFllV0fydZmx4bgOWzVuG5Kokls1bhg3PbUDzd5rTvqRuf2EnjplY1uvvvuJu43/U5L1CK3OTyWTWKZvFfn8r0VHnC1hRxTGAAjwvoqbp/z3YezDtstrNKaiqipFVI8t6j2unXVt2O2g4v689yVfDZtastdi/f6XjxU2VGEA2XXTNryJ1BeD0rKLSA08igl2f2YVlH1yGDc9vQGxNDBue34CrLrwKb559EyufWunqFNQz75zBw+0P48qHr8SSny1BTJx9DcaPGo8r33el43ZQdm5NCa60XGmeWCzmeMpmKTV/Svl7trehp6cNO3fGQrOGoByRCQB+/6MSEaxvXZ9234tLXhwcc4itiaVN73Tyhf3dW7/Dsi3LULe2Dl/8+RdRHavGtz7+LWy8eaOjNp86fwr3Pn1v5POobvJy7Um5cq3MVVXU1yfSOlarAy50Bl9sR+0knePXcQmTIpECcmuefaXbmK3m/9qPrS1rCmpfsg8/ffWn2PjCRvyi+xcYERuBuz5wF+655h40T20evMT+yb6flLYSeFYrLnnvJdwi0mVerT0pl5MaNqVMZ85X88dpOieMpSTKFZlpoF5O5yxVvgVcjVMa0Xm0c/C5xbb5zTNv4tsvfhvf7PgmDp86jKnjp+ILTV/AZ+d8FhfWXDjs+cXUArK0zmrFE3c9gbEjxg4GENO/w7Bxa0pwJVWqPn8xU0hLnWYa9lIShUR+Gqj9zMrih84fyD7baO3H1g52/sVOQVVV/Odr/4m7f3Q3pq2bhr/f/ve4dNKl+NFdP0J3vBv3XX9f1s4fAGpG1mDzpzbj0dsfRX1tfdbn1NfW4/ZLb8cl770krfP3UyotDJyU8zDBaZonn8yOuqUlOZgOsqd8Sk3nhL2UhFORSAEBuf+o/BIEMmcbxWIx3HbJbWiZ0VJwVe/Zd87i+7/5Ph5sfxCdRzsxftR4fGHuF3DPNffg/ZPen/Y5+WaXiAgWNS7CwisXYs+be7Dr8K7B/QCapzXjyvddiXufvhdtz7UNtsdvqbSgc1LOwyS3J0gUWx3TSTqHpSSyUB2+U+ynCfIAAAijSURBVLxff5qamtSJZDKp8S1xRQIa3xLPetuvMttmv73vrX26fMtyrf16rSIBvfzBy/WhFx7S0+dPZ32v1dtXpx2v9XtYvX11Se2xfnfWj99/h0Hjxv+noMv3vU8mk7pvX1y3b4fu25f6Pb366rK02/w+pgPQrln61EhcATipkeMXmW1LahI/3/dzbHxhI57a/xSqY9W4Y/YdWHrNUlw3/bqcx6IuDYR7URsp6jxfe+JD+a4sMq8SDh68HyLAxRcvQ3V1LQAEfq9gr0RmEBjw/+KafI6dPYbvvPQdPNz+MA6dPISLx12Mzzd9Hp+b8zlcNO6iot5DXRgId+M9iNxg9V1DYwbL0NCwPlKDu8XKNQgcqQAQRM/3PI8Hnn8Aj7/8OM73n0fLjBYs/eBS3H7p7RhRNaLk99MyZpfYO//M/DSDAJmieWYEAawYCnBP4EA59+45/OC3P8CD7Q+i/fV21IyswWeu/gy+eM0Xcfn7Lnf8vlYHblfKQHiQU2kUXrnWDQBMBRXCAOAjB04cwEMvPITvdn4Xx88dx+xJs/HAzQ9g4VULMX7U+LLeO9/ZO1B8Hp/5afKb1BVAeiXdrq7lUAVef31DpGv9FMIA4JFc4w9JTWJr11ZsfGEjtvx+C2ISwyfe/wncc809uGHmDa59ad08e690bSSiYg2lfzagrm7ZYKff07MBQGpgON/isKCOCbqFYwAeSOxIYPeR3dj71l4cPnkY0yZMQ8MFDaiKVWH/if04cOIALhx7IZY0LcGSpiWYOn5qxdrCL335+Dv0F/uKZADYuXNojKulJZl1tXqlVjH7lS/HAETkXgDfADBZVd8y2ZZKUVXsPrIb2/ZvG7zvtZOv4bWTrwEArpt2Hb5249dwx+w7yi7NXAyevZcnsSOB3rd7B6+irNSaWyW6yxXF4GQt8AKQdXGYVZ7a6tzVYS2hMDIWAERkGoCbALxmqg1eEBHsfWtv1scuqrkIv1z8S49bRE65tZaiUvwenCrN6sTHjm3E2bOdGDu2ET09bejt3YmzZzvTOvdcexlEbdqoyVpA6wB8GUNboITW4ZOHs95/9MxRj1tC5bDGTdws0e0We3DyexnpSrAvDmtq6kBdXRxnz6aKKFqdf7a6RXZR6/wBQ2MAInIbgI+oalxEDgKYmysFJCJLACwBgOnTpzcdOnTIu4a6ZMb6GYMpH7vpE6bj0PLgHU/UlbOWopK4SC99U/psYwH250Vp03rPq4GKyDMi8tssP7cDuA/AqmLeR1UfUdW5qjp38uTJlWpuxagqZk+anfWx2ZNmh/7MzJJ5nEE97lxrKfxwPH6ueOsV+4CuXbbNYwpVHI2CigUAVf2oql6e+QPgAIB6AHsGzv6nAnhRRKZUqi0miQjmT52PBbMWYPqE6RAIpk+YjgWzFmD+1PmR+OP0+25sxcpcS1FMiW4T7bPzQ7u8VEznztLQQzwfBFbV3wB4n3W7UAooDDIXTwHRmJ0B+H/gtBR+Xgnt1kK/oCu2nDRLQ6cYXwdQSgAI6jqAqAtbbtqvwTzqs4Ds/Pr/yBQWgyOj/DpwGjbs+CgbzweBiSzMTXuHC/2oFAwAVFF+HzglijIWg6OK8vPAKVHUcQyAPMHcNJE5HAMgo5ibJvIfBgAioohiACAiiigGACKiiGIAICKKKAYAIqKIYgAgIoooBgAioogK1EIwETkGIApbaE0CENry2Bl4rOEUlWMNynHOUNVhO2oFKgBEhYi0Z1u1F0Y81nCKyrEG/TiZAiIiiigGACKiiGIA8KdHTDfAQzzWcIrKsQb6ODkGQEQUUbwCICKKKAYAIqKIYgDwORG5V0RURCaZbkuliMg3ROR3IvJrEfmxiNSabpObRKRVRF4VkS4R+VvT7akUEZkmIttFZK+IvCwicdNtqjQRqRKRl0TkZ6bb4gQDgI+JyDQANwF4zXRbKuxpAJer6pUA9gH4iuH2uEZEqgBsBHAzgMsA/IWIXGa2VRXTB+CvVXU2gPkAvhTiY7XEAew13QinGAD8bR2ALwMI9Ui9qj6lqn0DN3cDmGqyPS77IIAuVT2gqu8A+AGA2w23qSJU9Q1VfXHg36eR6hjrzLaqckRkKoA/BfBt021xigHAp0TkNgA9qrrHdFs8thjAFtONcFEdgMO220cQ4k7RIiIzAVwN4DmzLamo9UidoCVNN8SpatMNiDIReQbAlCwP3Qfg7wB8zNsWVU6+Y1XVnww85z6k0gjf87JtFZZt8+NQX9GJSA2AHwJYrqqnTLenEkTkVgB/UNUOEfmw6fY4xQBgkKp+NNv9InIFgHoAewY2T58K4EUR+aCqHvWwia7JdawWEfk0gFsBfETDtTjlCIBptttTAbxuqC0VJyIjkOr8v6eqPzLdngr6EIDbROQWAKMBjBeRf1fVvzTcrpJwIVgAiMhBAHNVNQhVB0smIq0A1gJoUdVjptvjJhGpRmpg+yMAegC8AOBTqvqy0YZVgKTOVh4DcFxVl5tuj1cGrgDuVdVbTbelVBwDID94AMA4AE+LSKeIPGy6QW4ZGNxeCmAbUoOij4ex8x/wIQALAdw48P+xc+AMmXyKVwBERBHFKwAioohiACAiiigGACKiiGIAICKKKAYAIqKIYgAgKsFAxctuEZk4cPuCgdszRGSriPQGtTIkRQ8DAFEJVPUwgIcAfH3grq8DeERVDwH4BlLz4IkCgQGAqHTrAMwXkeUArgPwPwFAVX8B4LTJhhGVgrWAiEqkqu+KyP8AsBXAxwbKPBMFDq8AiJy5GcAbAC433RAipxgAiEokIo1I7dQ2H8AKEbnIcJOIHGEAICrBQMXLh5Cqdf8aUgO//2K2VUTOMAAQleZzAF5T1acHbj8I4P0i0iIivwTwBICPiMgREVlgrJVERWA1UCKiiOIVABFRRDEAEBFFFAMAEVFEMQAQEUUUAwARUUQxABARRRQDABFRRP1/HsTDtLi3qCwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (6,4)\n",
    "centroids,idx,cent,cnt = kmeans(load_data(filename_widget.value),K_widget.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ac002a78a3f487780183ff7f0cf73ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Play(value=1, description='Press play', interval=2000, max=10, min=1), IntSlider…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def choose_display(n):\n",
    "    pylab.rcParams['figure.figsize'] = (10,10)\n",
    "    a=plt.imread(\"figure %d.jpg\"%(n))\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(a)\n",
    "\n",
    "n_widget=widgets.IntSlider(min=1,max=cnt,description='迭代次数')\n",
    "play_widget = widgets.Play(\n",
    "    interval=2000,\n",
    "    value=1,\n",
    "    min=1,\n",
    "    max=cnt,\n",
    "    step=1,\n",
    "    description=\"Press play\",\n",
    "    disabled=False\n",
    ")\n",
    "widgets.jslink((play_widget, 'value'), (n_widget, 'value'))\n",
    "out = widgets.interactive_output(choose_display, {'n': n_widget})\n",
    "widgets.VBox([widgets.HBox([play_widget,n_widget]), out])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}